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Capacity(调优系统容量)六性能优化调优基础篇这是我的小站欢迎访问哦说明性能能力系统容量文中翻译为系统容量意为硬件配置调优和其他性能调优是同样的原理初学者可能会被多个参数弄得一头雾水然后随便调整几个来试试结果又或者修改几行代码来测试其实只要参照下面的步骤就能保证你的调优方向正确列出性能调优指标执行测试检查结果与目标进行对比如果达不到指标修改配置参数然后继续测试第一步我们需要做的事情就是制定明确的性能指标对所有性能监控和管理来说有三个维度是通用的延迟吞吐量系统容量我们先讲解基本概念然后再演示如何使用这些指标如果您对延迟吞吐量和系统容量等概念很熟悉可以跳过这一小节核心概念我们先来看一家工厂的装配流水线工人在流水线将现成的组件按顺序拼接组装成自行车通过实地观测我们发现从组件进入生产线到另一端组装成自行车需要小时继续观察我们还发现此后每分钟就有辆自行车完成组装每天小时一直如此将这个模型简化并忽略维护窗口期后得出结论这条流水线每小时可以组装辆自行车说明时间窗口窗口期请类比车站卖票的窗口是一段规定限定做某件事的时间段通过这两种测量方法就知道了生产线的相关性能信息延迟与吞吐量生产线的延迟小时生产线的吞吐量辆小时请注意衡量延迟的时间单位根据具体需要而确定从纳秒到几千年都有可能系统的吞吐量是每个单位时间内完成的操作操作一般是特定系统相关的东西在本例中选择的时间单位是小时操作就是对自行车的组装掌握了延迟和吞吐量两个概念之后让我们对这个工厂来进行实际的调优自行车的需求在一段时间内都很稳定生产线组装自行车有四个小时延迟而吞吐量在几个月以来都很稳定辆小时假设某个销售团队突然业绩暴涨对自行车的需求增加了倍客户每天需要的自行车不再是辆而是辆天老板对工厂的产能不满意想要做些调整以提升产能看起来总经理很容易得出正确的判断系统的延迟没法子进行处理他关注的是每天的自行车生产总量得出这个结论以后假若工厂资金充足那么应该立即采取措施改善吞吐量以增加产能我们很快会看到这家工厂有两条相同的生产线每条生产线一分钟可以组装一辆成品自行车可以想象每天生产的自行车数量会增加一倍达到辆天要注意的是不需要减少自行车的装配时间从开始到结束依然需要小时巧合的是这样进行的性能优化同时增加了吞吐量和产能一般来说我们会先测量当前的系统性能再设定新目标只优化系统的某个方面来满足性能指标在这里做了一个很重要的决定要增加吞吐量而不是减小延迟在增加吞吐量的同时也需要增加系统容量比起原来的情况现在需要两条流水线来生产出所需的自行车在这种情况下增加系统的吞吐量并不是免费的需要水平扩展以满足增加的吞吐量需求在处理性能问题时应该考虑到还有另一种看似不相关的解决办法假如生产线的延迟从分钟降低为秒那么吞吐量同样可以增长倍或者是降低延迟或者是客户非常有钱软件工程里有一种相似的说法每个性能问题背后总有两种不同的解决办法可以用更多的机器或者是花精力来改善性能低下的代码延迟的延迟指标由一般的延迟需求决定延迟指标通常如下所述所有交易必须在秒内得到响应的订单付款操作必须在秒以内处理完成推荐商品必须在内展示到用户面前面对这类性能指标时需要确保在交易过程中暂停不能占用太多时间否则就满足不了指标不能占用太多的意思需要视具体情况而定还要考虑到其他因素比如外部数据源的交互时间锁竞争以及其他的安全点等等假设性能需求为的交易要在以内完成每次交易最长不能超过秒根据经验假设暂停时间比例不能超过也就是说的暂停必须在内结束也不能有超过的暂停为简单起见我们忽略在同一次交易过程中发生多次停顿的可能性有了正式的需求下一步就是检查暂停时间有许多工具可以使用在本节中我们通过查看日志检查一下暂停的时间相关的信息散落在不同的日志片段中看下面的数据这表示一次暂停在这个时刻触发对应于启动之后的此事件将应用线程暂停了秒虽然花费的总时间为但因为是多核机器所以最重要的数字是应用线程被暂停的总时间这里使用的是并行所以暂停时间大约为这次的暂停时间小于的阈值满足需求继续分析从所有日志中提取出暂停相关的数据汇总之后就可以得知是否满足需求吞吐量吞吐量和延迟指标有很大区别当然两者都是根据一般吞吐量需求而得出的一般吞吐量需求类似这样解决方案每天必须处理万个订单解决方案必须支持个登录用户同时在秒内执行某个操作或每周对所有客户进行统计时间不能超过小时时间窗口为每周日晚点到次日点之间可以看出吞吐量需求不是针对单个操作的而是在给定的时间内系统必须完成多少个操作和延迟需求类似调优也需要确定行为所消耗的总时间每个系统能接受的时间不同一般来说占用的总时间比不能超过现在假设需求为每分钟处理笔交易同时每分钟暂停的总时间不能超过秒即有了正式的需求下一步就是获取相关的信息依然是从日志中提取数据可以看到类似这样的信息此时我们对用户耗时和系统耗时感兴趣而不关心实际耗时在这里我们关心的时间为这段时间内暂停占用了资源重要的是系统运行在多核机器上转换为实际的停顿时间为秒下面的计算会用到这个数字提取出有用的信息后剩下要做的就是统计每分钟内暂停的总时间看看是否满足需求每分钟内总的暂停时间不得超过毫秒秒系统容量系统容量需求是在达成吞吐量和延迟指标的情况下对硬件环境的额外约束这类需求大多是来源于计算资源或者预算方面的原因例如系统必须能部署到小于内存的设备上系统必须部署在实例上配置不得超过核每月的账单不得超过因此在满足延迟和吞吐量需求的基础上必须考虑系统容量可以说假若有无限的计算资源可供挥霍那么任何延迟和吞吐量指标都不成问题但现实情况是预算和其他约束限制了可用的资源相关示例介绍完性能调优的三个维度后我们来进行实际的操作以达成性能指标请看下面的代码这段程序代码每毫秒提交两个作业来每个作业都模拟特定的生命周期创建对象然后在预定的时间释放接着就不管了由来自动回收占用的内存在运行这个示例程序时通过以下参数打开日志记录还应该加上参数以指定日志的存储位置类似这样在日志文件中可以看到的行为类似下面这样基于日志中的信息可以通过三个优化目标来提升性能确保最坏情况下暂停时间不超过预定阀值确保线程暂停的总时间不超过预定阀值在确保达到延迟和吞吐量指标的情况下降低硬件配置以及成本为此用三种不同的配置将代码运行分钟得到了三种不同的结果汇总如下堆内存大小算法有效时间比最长停顿时间使用不同的算法和不同的内存配置运行相同的代码以测量暂停时间与延迟吞吐量的关系实验的细节和结果在后面章节详细介绍注意为了尽量简单示例中只改变了很少的输入参数此实验也没有在不同数量或者不同的堆布局下进行测试调优延迟指标假设有一个需求每次作业必须在内处理完成我们知道实际的作业处理只需要简化后两者相减就可以算出对暂停的延迟要求现在需求变成暂停不能超过这个问题很容易找到答案只需要解析日志文件并找出暂停中最大的那个暂停时间即可再来看测试所用的三个配置堆内存大小算法有效时间比最长停顿时间可以看到其中有一个配置达到了要求运行的参数为对应的日志中暂停时间最大为这达到了延迟指标的要求如果还满足吞吐量和系统容量需求的话就可以说成功达成了调优目标调优结束吞吐量调优假定吞吐量指标为每小时完成万次操作处理同样是上面的配置其中有一种配置满足了需求堆内存大小算法有效时间比最长停顿时间此配置对应的命令行参数为可以看到占用了的时间剩下的是有效的计算时间为简单起见忽略示例中的其他安全点现在需要考虑每个核心处理一次作业需要耗时因此一分钟内每个核心可以执行次操作每个完成次操作一小时内一个核心可以执行万次操作有四个内核则每小时可以执行万次操作理论上通过简单的计算就可以得出结论每小时可以执行的操作数为次满足需求值得一提的是假若还要满足延迟指标那就有问题了最坏情况下暂停时间为最大延迟时间是前一种配置的两倍调优系统容量假设需要将软件部署到服务器上配置为核这样的话系统容量的要求就变成最大的堆内存空间不能超过有了这个需求我们需要调整为第三套配置进行测试堆内存大小算法有效时间比最长停顿时间程序可以通过如下参数执行测试结果是延迟大幅增长吞吐量同样大幅降低现在占用了更多的资源这个配置只有的有效时间因此这个配置让吞吐量从最好的情况操作小时下降到不足次操作小时最坏情况下的延迟变成了而不再是通过对这三个维度的介绍你应该了解不是简单的进行性能优化而是需要从三种不同的维度来进行考虑测量并调优延迟和吞吐量此外还需要考虑系统容量的约束",isPost:!0,isHome:!1,isHighlightShrink:!0,isToc:!0,postUpdate:"2019-01-29 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id="article-container" itemscope itemtype="https://it985.github.io/posts/1cbf2ab0.html"><header><a href="/tags/JVM/" tabindex="-1" itemprop="url">JVM</a><h1 id="CrawlerTitle" itemprop="name headline">六、GC 性能优化 – GC 调优(基础篇)</h1><span itemprop="author" itemscope itemtype="http://schema.org/Person">云少</span><time itemprop="dateCreated datePublished" datetime="2019-01-29T15:03:48.000Z" title="发表于 2019-01-29 23:03:48">2019-01-29</time><time itemprop="dateCreated datePublished" datetime="2019-01-29T15:03:48.000Z" title="更新于 2019-01-29 23:03:48">2019-01-29</time></header><h1 id="六、GC-性能优化-–-GC-调优-基础篇"><a href="#六、GC-性能优化-–-GC-调优-基础篇" class="headerlink" title="六、GC 性能优化 – GC 调优(基础篇)"></a>六、GC 性能优化 – GC 调优(基础篇)</h1><blockquote><p>master ，这是我的<a target="_blank" rel="noopener external nofollow noreferrer" href="https://www.tryrun.top/">小站</a>,欢迎访问哦~~</p></blockquote><p><strong>说明</strong>:</p><p><strong>Capacity</strong>: 性能,能力,系统容量; 文中翻译为”<strong>系统容量</strong>“; 意为硬件配置。</p><p>GC调优(Tuning Garbage Collection)和其他性能调优是同样的原理。初学者可能会被 200 多个 GC参数弄得一头雾水, 然后随便调整几个来试试结果,又或者修改几行代码来测试。其实只要参照下面的步骤，就能保证你的调优方向正确:</p><p>1、列出性能调优指标(State your performance goals)</p><p>2、执行测试(Run tests)</p><p>3、检查结果(Measure the results)</p><p>4、与目标进行对比(Compare the results with the goals)</p><p>5、如果达不到指标, 修改配置参数, 然后继续测试(go back to running tests)</p><p>第一步, 我们需要做的事情就是: 制定明确的GC性能指标。对所有性能监控和管理来说, 有三个维度是通用的:</p><ul><li>Latency(延迟)</li><li>Throughput(吞吐量)</li><li>Capacity(系统容量)</li></ul><p>我们先讲解基本概念,然后再演示如何使用这些指标。如果您对 延迟、吞吐量和系统容量等概念很熟悉, 可以跳过这一小节。</p><h2 id="核心概念-Core-Concepts"><a href="#核心概念-Core-Concepts" class="headerlink" title="核心概念(Core Concepts)"></a>核心概念(Core Concepts)</h2><p>我们先来看一家工厂的装配流水线。工人在流水线将现成的组件按顺序拼接,组装成自行车。通过实地观测, 我们发现从组件进入生产线，到另一端组装成自行车需要4小时。</p><p><img src= "" onerror="this.onerror=null,this.src=&quot;/img/404.jpg&quot;" data-lazy-src="https://s3.uuu.ovh/imgs/2022/05/09/ba8a3240d2218ca0.png" alt="img_1.png"></p><p>继续观察,我们还发现,此后每分钟就有1辆自行车完成组装, 每天24小时,一直如此。将这个模型简化, 并忽略维护窗口期后得出结论： <strong>这条流水线每小时可以组装60辆自行车</strong>。</p><blockquote><p><strong>说明</strong>: 时间窗口&#x2F;窗口期，请类比车站卖票的窗口，是一段规定&#x2F;限定做某件事的时间段。</p></blockquote><p>通过这两种测量方法, 就知道了生产线的相关性能信息： <strong>延迟</strong>与<strong>吞吐量</strong>:</p><ul><li>生产线的延迟: <strong>4小时</strong></li><li>生产线的吞吐量: <strong>60辆&#x2F;小时</strong></li></ul><p>请注意, 衡量延迟的时间单位根据具体需要而确定 —— 从纳秒(nanosecond)到几千年(millennia)都有可能。系统的吞吐量是每个单位时间内完成的操作。操作(Operations)一般是特定系统相关的东西。在本例中,选择的时间单位是小时, 操作就是对自行车的组装。</p><p>掌握了延迟和吞吐量两个概念之后, 让我们对这个工厂来进行实际的调优。自行车的需求在一段时间内都很稳定, 生产线组装自行车有四个小时延迟, 而吞吐量在几个月以来都很稳定: 60辆&#x2F;小时。假设某个销售团队突然业绩暴涨, 对自行车的需求增加了1倍。客户每天需要的自行车不再是 60 * 24 &#x3D; 1440辆, 而是 2*1440 &#x3D; 2880辆&#x2F;天。老板对工厂的产能不满意，想要做些调整以提升产能。</p><p>看起来总经理很容易得出正确的判断, 系统的延迟没法子进行处理 —— 他关注的是每天的自行车生产总量。得出这个结论以后, 假若工厂资金充足, 那么应该立即采取措施, 改善吞吐量以增加产能。</p><p>我们很快会看到, 这家工厂有两条相同的生产线。每条生产线一分钟可以组装一辆成品自行车。 可以想象，每天生产的自行车数量会增加一倍。达到 2880辆&#x2F;天。要注意的是, 不需要减少自行车的装配时间 —— 从开始到结束依然需要 4 小时。</p><p><img src= "" onerror="this.onerror=null,this.src=&quot;/img/404.jpg&quot;" data-lazy-src="https://s3.uuu.ovh/imgs/2022/05/09/b2beb2733fbec5e2.png" alt="img_2.png"></p><p>巧合的是，这样进行的性能优化,同时增加了吞吐量和产能。一般来说，我们会先测量当前的系统性能, 再设定新目标, 只优化系统的某个方面来满足性能指标。</p><p>在这里做了一个很重要的决定 —— 要增加吞吐量,而不是减小延迟。在增加吞吐量的同时, 也需要增加系统容量。比起原来的情况, 现在需要两条流水线来生产出所需的自行车。在这种情况下, 增加系统的吞吐量并不是免费的, 需要水平扩展, 以满足增加的吞吐量需求。</p><p>在处理性能问题时, 应该考虑到还有另一种看似不相关的解决办法。假如生产线的延迟从1分钟降低为30秒,那么吞吐量同样可以增长 1 倍。</p><p>或者是降低延迟, 或者是客户非常有钱。软件工程里有一种相似的说法 —— 每个性能问题背后,总有两种不同的解决办法。 可以用更多的机器, 或者是花精力来改善性能低下的代码。</p><h3 id="Latency-延迟"><a href="#Latency-延迟" class="headerlink" title="Latency(延迟)"></a>Latency(延迟)</h3><p>GC的延迟指标由一般的延迟需求决定。延迟指标通常如下所述:</p><ul><li>所有交易必须在10秒内得到响应</li><li>90%的订单付款操作必须在3秒以内处理完成</li><li>推荐商品必须在 100 ms 内展示到用户面前</li></ul><p>面对这类性能指标时, 需要确保在交易过程中, GC暂停不能占用太多时间，否则就满足不了指标。“不能占用太多” 的意思需要视具体情况而定, 还要考虑到其他因素, 比如外部数据源的交互时间(round-trips), 锁竞争(lock contention), 以及其他的安全点等等。</p><p>假设性能需求为: <code>90%</code>的交易要在 <code>1000ms</code> 以内完成, 每次交易最长不能超过 <code>10秒</code>。 根据经验, 假设GC暂停时间比例不能超过10%。 也就是说, 90%的GC暂停必须在 <code>100ms</code> 内结束, 也不能有超过 <code>1000ms</code> 的GC暂停。为简单起见, 我们忽略在同一次交易过程中发生多次GC停顿的可能性。</p><p>有了正式的需求,下一步就是检查暂停时间。有许多工具可以使用, 在本节中我们通过查看GC日志, 检查一下GC暂停的时间。相关的信息散落在不同的日志片段中, 看下面的数据:</p><figure class="highlight plaintext"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br></pre></td><td class="code"><pre><span class="line">2022-05-09T13:34:16.974-0200: 2.578: [Full GC (Ergonomics)</span><br><span class="line">        [PSYoungGen: 93677K-&gt;70109K(254976K)] </span><br><span class="line">        [ParOldGen: 499597K-&gt;511230K(761856K)] </span><br><span class="line">        593275K-&gt;581339K(1016832K),</span><br><span class="line">        [Metaspace: 2936K-&gt;2936K(1056768K)]</span><br><span class="line">    , 0.0713174 secs]</span><br><span class="line">    [Times: user=0.21 sys=0.02, real=0.07 secs</span><br></pre></td></tr></table></figure><p>这表示一次GC暂停, 在 <code>2015-06-04T13:34:16</code> 这个时刻触发. 对应于JVM启动之后的 <code>2,578 ms</code>。</p><p>此事件将应用线程暂停了 <code>0.0713174</code> 秒。虽然花费的总时间为 210 ms, 但因为是多核CPU机器, 所以最重要的数字是应用线程被暂停的总时间, 这里使用的是并行GC, 所以暂停时间大约为 <code>70ms</code> 。 这次GC的暂停时间小于 <code>100ms</code> 的阈值，满足需求。</p><p>继续分析, 从所有GC日志中提取出暂停相关的数据, 汇总之后就可以得知是否满足需求。</p><h3 id="Throughput-吞吐量"><a href="#Throughput-吞吐量" class="headerlink" title="Throughput(吞吐量)"></a>Throughput(吞吐量)</h3><p>吞吐量和延迟指标有很大区别。当然两者都是根据一般吞吐量需求而得出的。一般吞吐量需求(Generic requirements for throughput) 类似这样:</p><ul><li>解决方案每天必须处理 100万个订单</li><li>解决方案必须支持1000个登录用户,同时在5-10秒内执行某个操作: A、B或C</li><li>每周对所有客户进行统计, 时间不能超过6小时，时间窗口为每周日晚12点到次日6点之间。</li></ul><p>可以看出,吞吐量需求不是针对单个操作的, 而是在给定的时间内, 系统必须完成多少个操作。和延迟需求类似, GC调优也需要确定GC行为所消耗的总时间。每个系统能接受的时间不同, 一般来说, GC占用的总时间比不能超过 <code>10%</code>。</p><p>现在假设需求为: 每分钟处理 1000 笔交易。同时, 每分钟GC暂停的总时间不能超过6秒(即10%)。</p><p>有了正式的需求, 下一步就是获取相关的信息。依然是从GC日志中提取数据, 可以看到类似这样的信息:</p><figure class="highlight plaintext"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br></pre></td><td class="code"><pre><span class="line">2022-05-09T13:34:16.974-0200: 2.578: [Full GC (Ergonomics)</span><br><span class="line">        [PSYoungGen: 93677K-&gt;70109K(254976K)] </span><br><span class="line">        [ParOldGen: 499597K-&gt;511230K(761856K)] </span><br><span class="line">        593275K-&gt;581339K(1016832K), </span><br><span class="line">        [Metaspace: 2936K-&gt;2936K(1056768K)], </span><br><span class="line">     0.0713174 secs] </span><br><span class="line">     [Times: user=0.21 sys=0.02, real=0.07 secs</span><br></pre></td></tr></table></figure><p>此时我们对 用户耗时(user)和系统耗时(sys)感兴趣, 而不关心实际耗时(real)。在这里, 我们关心的时间为 <code>0.23s</code>(user + sys &#x3D; 0.21 + 0.02 s), 这段时间内, GC暂停占用了 cpu 资源。 重要的是, 系统运行在多核机器上, 转换为实际的停顿时间(stop-the-world)为 <code>0.0713174秒</code>, 下面的计算会用到这个数字。</p><p>提取出有用的信息后, 剩下要做的就是统计每分钟内GC暂停的总时间。看看是否满足需求: 每分钟内总的暂停时间不得超过6000毫秒(6秒)。</p><h3 id="Capacity-系统容量"><a href="#Capacity-系统容量" class="headerlink" title="Capacity(系统容量)"></a>Capacity(系统容量)</h3><p>系统容量(Capacity)需求,是在达成吞吐量和延迟指标的情况下,对硬件环境的额外约束。这类需求大多是来源于计算资源或者预算方面的原因。例如:</p><ul><li>系统必须能部署到小于512 MB内存的Android设备上</li><li>系统必须部署在Amazon <strong>EC2</strong>实例上, 配置不得超过 **c3.xlarge(4核8GB)**。</li><li>每月的 Amazon EC2 账单不得超过 <code>$12,000</code></li></ul><p>因此, 在满足延迟和吞吐量需求的基础上必须考虑系统容量。可以说, 假若有无限的计算资源可供挥霍, 那么任何 延迟和吞吐量指标 都不成问题, 但现实情况是, 预算(budget)和其他约束限制了可用的资源。</p><h2 id="相关示例"><a href="#相关示例" class="headerlink" title="相关示例"></a>相关示例</h2><p>介绍完性能调优的三个维度后, 我们来进行实际的操作以达成GC性能指标。</p><p>请看下面的代码:</p><figure class="highlight java"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br><span class="line">39</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment">//imports skipped for brevity</span></span><br><span class="line"><span class="keyword">public</span> <span class="keyword">class</span> <span class="title class_">Producer</span> <span class="keyword">implements</span> <span class="title class_">Runnable</span> &#123;</span><br><span class="line"></span><br><span class="line">  <span class="keyword">private</span> <span class="keyword">static</span> <span class="type">ScheduledExecutorService</span> <span class="variable">executorService</span></span><br><span class="line">         <span class="operator">=</span> Executors.newScheduledThreadPool(<span class="number">2</span>);</span><br><span class="line"></span><br><span class="line">  <span class="keyword">private</span> Deque&lt;<span class="type">byte</span>[]&gt; deque;</span><br><span class="line">  <span class="keyword">private</span> <span class="type">int</span> objectSize;</span><br><span class="line">  <span class="keyword">private</span> <span class="type">int</span> queueSize;</span><br><span class="line"></span><br><span class="line">  <span class="keyword">public</span> <span class="title function_">Producer</span><span class="params">(<span class="type">int</span> objectSize, <span class="type">int</span> ttl)</span> &#123;</span><br><span class="line">    <span class="built_in">this</span>.deque = <span class="keyword">new</span> <span class="title class_">ArrayDeque</span>&lt;<span class="type">byte</span>[]&gt;();</span><br><span class="line">    <span class="built_in">this</span>.objectSize = objectSize;</span><br><span class="line">    <span class="built_in">this</span>.queueSize = ttl * <span class="number">1000</span>;</span><br><span class="line">  &#125;</span><br><span class="line"></span><br><span class="line">  <span class="meta">@Override</span></span><br><span class="line">  <span class="keyword">public</span> <span class="keyword">void</span> <span class="title function_">run</span><span class="params">()</span> &#123;</span><br><span class="line">    <span class="keyword">for</span> (<span class="type">int</span> <span class="variable">i</span> <span class="operator">=</span> <span class="number">0</span>; i &lt; <span class="number">100</span>; i++) &#123; </span><br><span class="line">        deque.add(<span class="keyword">new</span> <span class="title class_">byte</span>[objectSize]); </span><br><span class="line">        <span class="keyword">if</span> (deque.size() &gt; queueSize) &#123;</span><br><span class="line">            deque.poll();</span><br><span class="line">        &#125;</span><br><span class="line">    &#125;</span><br><span class="line">  &#125;</span><br><span class="line"></span><br><span class="line">  <span class="keyword">public</span> <span class="keyword">static</span> <span class="keyword">void</span> <span class="title function_">main</span><span class="params">(String[] args)</span> </span><br><span class="line">        <span class="keyword">throws</span> InterruptedException &#123;</span><br><span class="line">    executorService.scheduleAtFixedRate(</span><br><span class="line">        <span class="keyword">new</span> <span class="title class_">Producer</span>(<span class="number">200</span> * <span class="number">1024</span> * <span class="number">1024</span> / <span class="number">1000</span>, <span class="number">5</span>), </span><br><span class="line">        <span class="number">0</span>, <span class="number">100</span>, TimeUnit.MILLISECONDS</span><br><span class="line">    );</span><br><span class="line">    executorService.scheduleAtFixedRate(</span><br><span class="line">        <span class="keyword">new</span> <span class="title class_">Producer</span>(<span class="number">50</span> * <span class="number">1024</span> * <span class="number">1024</span> / <span class="number">1000</span>, <span class="number">120</span>), </span><br><span class="line">        <span class="number">0</span>, <span class="number">100</span>, TimeUnit.MILLISECONDS);</span><br><span class="line">    TimeUnit.MINUTES.sleep(<span class="number">10</span>);</span><br><span class="line">    executorService.shutdownNow();</span><br><span class="line">  &#125;</span><br><span class="line">&#125;</span><br></pre></td></tr></table></figure><p>这段程序代码, 每 100毫秒 提交两个作业(job)来。每个作业都模拟特定的生命周期: 创建对象, 然后在预定的时间释放, 接着就不管了, 由GC来自动回收占用的内存。</p><p>在运行这个示例程序时，通过以下JVM参数打开GC日志记录:</p><figure class="highlight plaintext"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">-XX:+PrintGCDetails -XX:+PrintGCDateStamps -XX:+PrintGCTimeStamps</span><br></pre></td></tr></table></figure><p>还应该加上JVM参数 <code>-Xloggc</code>以指定GC日志的存储位置,类似这样:</p><figure class="highlight plaintext"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">-Xloggc:C:\\Producer_gc.log</span><br></pre></td></tr></table></figure><p>在日志文件中可以看到GC的行为, 类似下面这样:</p><figure class="highlight plaintext"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br></pre></td><td class="code"><pre><span class="line">2022-05-09T13:34:16.119-0200: 1.723: [GC (Allocation Failure) </span><br><span class="line">        [PSYoungGen: 114016K-&gt;73191K(234496K)] </span><br><span class="line">    421540K-&gt;421269K(745984K), </span><br><span class="line">    0.0858176 secs] </span><br><span class="line">    [Times: user=0.04 sys=0.06, real=0.09 secs] </span><br><span class="line"></span><br><span class="line">2022-05-09T13:34:16.738-0200: 2.342: [GC (Allocation Failure) </span><br><span class="line">        [PSYoungGen: 234462K-&gt;93677K(254976K)] </span><br><span class="line">    582540K-&gt;593275K(766464K), </span><br><span class="line">    0.2357086 secs] </span><br><span class="line">    [Times: user=0.11 sys=0.14, real=0.24 secs] </span><br><span class="line"></span><br><span class="line">2022-05-09T13:34:16.974-0200: 2.578: [Full GC (Ergonomics) </span><br><span class="line">        [PSYoungGen: 93677K-&gt;70109K(254976K)] </span><br><span class="line">        [ParOldGen: 499597K-&gt;511230K(761856K)] </span><br><span class="line">    593275K-&gt;581339K(1016832K), </span><br><span class="line">        [Metaspace: 2936K-&gt;2936K(1056768K)], </span><br><span class="line">    0.0713174 secs] </span><br><span class="line">    [Times: user=0.21 sys=0.02, real=0.07 secs]</span><br></pre></td></tr></table></figure><p>基于日志中的信息, 可以通过三个优化目标来提升性能:</p><p>1、 确保最坏情况下,GC暂停时间不超过预定阀值</p><p>2、 确保线程暂停的总时间不超过预定阀值</p><p>3、 在确保达到延迟和吞吐量指标的情况下, 降低硬件配置以及成本。</p><p>为此, 用三种不同的配置, 将代码运行10分钟, 得到了三种不同的结果, 汇总如下:</p><table><thead><tr><th align="center"><strong>堆内存大小(Heap)</strong></th><th align="center"><strong>GC算法(GC Algorithm)</strong></th><th align="center"><strong>有效时间比(Useful work)</strong></th><th align="center"><strong>最长停顿时间(Longest pause)</strong></th></tr></thead><tbody><tr><td align="center">-Xmx12g</td><td align="center">-XX:+UseConcMarkSweepGC</td><td align="center">89.8%</td><td align="center"><strong>560 ms</strong></td></tr><tr><td align="center">-Xmx12g</td><td align="center">-XX:+UseParallelGC</td><td align="center">91.5%</td><td align="center">1,104 ms</td></tr><tr><td align="center">-Xmx8g</td><td align="center">-XX:+UseConcMarkSweepGC</td><td align="center">66.3%</td><td align="center">1,610 ms</td></tr></tbody></table><p>使用不同的GC算法,和不同的内存配置,运行相同的代码, 以测量GC暂停时间与 延迟、吞吐量的关系。实验的细节和结果在后面章节详细介绍。</p><p>注意, 为了尽量简单, 示例中只改变了很少的输入参数, 此实验也没有在不同CPU数量或者不同的堆布局下进行测试。</p><h3 id="Tuning-for-Latency-调优延迟指标"><a href="#Tuning-for-Latency-调优延迟指标" class="headerlink" title="Tuning for Latency(调优延迟指标)"></a>Tuning for Latency(调优延迟指标)</h3><p>假设有一个需求, <strong>每次作业必须在 1000ms 内处理完成</strong>。我们知道, 实际的作业处理只需要100 ms，简化后， 两者相减就可以算出对 GC暂停的延迟要求。现在需求变成: <strong>GC暂停不能超过900ms</strong>。这个问题很容易找到答案, 只需要解析GC日志文件, 并找出GC暂停中最大的那个暂停时间即可。</p><p>再来看测试所用的三个配置:</p><table><thead><tr><th align="center"><strong>堆内存大小(Heap)</strong></th><th align="center"><strong>GC算法(GC Algorithm)</strong></th><th align="center"><strong>有效时间比(Useful work)</strong></th><th align="center"><strong>最长停顿时间(Longest pause)</strong></th></tr></thead><tbody><tr><td align="center">-Xmx12g</td><td align="center">-XX:+UseConcMarkSweepGC</td><td align="center">89.8%</td><td align="center"><strong>560 ms</strong></td></tr><tr><td align="center">-Xmx12g</td><td align="center">-XX:+UseParallelGC</td><td align="center">91.5%</td><td align="center">1,104 ms</td></tr><tr><td align="center">-Xmx8g</td><td align="center">-XX:+UseConcMarkSweepGC</td><td align="center">66.3%</td><td align="center">1,610 ms</td></tr></tbody></table><p>可以看到,其中有一个配置达到了要求。运行的参数为:</p><figure class="highlight plaintext"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">java -Xmx12g -XX:+UseConcMarkSweepGC Producer</span><br></pre></td></tr></table></figure><p>对应的GC日志中,暂停时间最大为 <code>560 ms</code>, 这达到了延迟指标 <code>900 ms</code> 的要求。如果还满足吞吐量和系统容量需求的话,就可以说成功达成了GC调优目标, 调优结束。</p><h3 id="Tuning-for-Throughput-吞吐量调优"><a href="#Tuning-for-Throughput-吞吐量调优" class="headerlink" title="Tuning for Throughput(吞吐量调优)"></a>Tuning for Throughput(吞吐量调优)</h3><p>假定吞吐量指标为: <strong>每小时完成 1300万次操作处理</strong>。同样是上面的配置, 其中有一种配置满足了需求:</p><table><thead><tr><th align="center"><strong>堆内存大小(Heap)</strong></th><th align="center"><strong>GC算法(GC Algorithm)</strong></th><th align="center"><strong>有效时间比(Useful work)</strong></th><th align="center"><strong>最长停顿时间(Longest pause)</strong></th></tr></thead><tbody><tr><td align="center">-Xmx12g</td><td align="center">-XX:+UseConcMarkSweepGC</td><td align="center">89.8%</td><td align="center">560 ms</td></tr><tr><td align="center">-Xmx12g</td><td align="center">-XX:+UseParallelGC</td><td align="center"><strong>91.5%</strong></td><td align="center">1,104 ms</td></tr><tr><td align="center">-Xmx8g</td><td align="center">-XX:+UseConcMarkSweepGC</td><td align="center">66.3%</td><td align="center">1,610 ms</td></tr></tbody></table><p>此配置对应的命令行参数为:</p><figure class="highlight plaintext"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">java -Xmx12g -XX:+UseParallelGC Producer</span><br></pre></td></tr></table></figure><p>可以看到,GC占用了 8.5%的CPU时间,剩下的 <code>91.5%</code> 是有效的计算时间。为简单起见, 忽略示例中的其他安全点。现在需要考虑:</p><p>1、 每个CPU核心处理一次作业需要耗时 <code>100ms</code></p><p>2、 因此, 一分钟内每个核心可以执行 60,000 次操作(<strong>每个job完成100次操作</strong>)</p><p>3、 一小时内, 一个核心可以执行 360万次操作</p><p>4、 有四个CPU内核, 则每小时可以执行: 4 x 3.6M &#x3D; 1440万次操作</p><p>理论上，通过简单的计算就可以得出结论, 每小时可以执行的操作数为: <code>14.4 M * 91.5% = 13,176,000</code> 次, 满足需求。</p><p>值得一提的是, 假若还要满足延迟指标, 那就有问题了, 最坏情况下, GC暂停时间为 <code>1,104 ms</code>, 最大延迟时间是前一种配置的两倍。</p><h3 id="Tuning-for-Capacity-调优系统容量"><a href="#Tuning-for-Capacity-调优系统容量" class="headerlink" title="Tuning for Capacity(调优系统容量)"></a>Tuning for Capacity(调优系统容量)</h3><p>假设需要将软件部署到服务器上(commodity-class hardware), 配置为 <code>4核10G</code>。这样的话, 系统容量的要求就变成: 最大的堆内存空间不能超过 <code>8GB</code>。有了这个需求, 我们需要调整为第三套配置进行测试:</p><table><thead><tr><th align="center"><strong>堆内存大小(Heap)</strong></th><th align="center"><strong>GC算法(GC Algorithm)</strong></th><th align="center"><strong>有效时间比(Useful work)</strong></th><th align="center"><strong>最长停顿时间(Longest pause)</strong></th></tr></thead><tbody><tr><td align="center">-Xmx12g</td><td align="center">-XX:+UseConcMarkSweepGC</td><td align="center">89.8%</td><td align="center">560 ms</td></tr><tr><td align="center">-Xmx12g</td><td align="center">-XX:+UseParallelGC</td><td align="center">91.5%</td><td align="center">1,104 ms</td></tr><tr><td align="center"><strong>-Xmx8g</strong></td><td align="center">-XX:+UseConcMarkSweepGC</td><td align="center">66.3%</td><td align="center">1,610 ms</td></tr></tbody></table><p>程序可以通过如下参数执行:</p><figure class="highlight plaintext"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">java -Xmx8g -XX:+UseConcMarkSweepGC Producer</span><br></pre></td></tr></table></figure><p>测试结果是延迟大幅增长, 吞吐量同样大幅降低:</p><ul><li>现在,GC占用了更多的CPU资源, 这个配置只有 <code>66.3%</code> 的有效CPU时间。因此,这个配置让吞吐量从最好的情况 <strong>13,176,000 操作&#x2F;小时</strong> 下降到 <strong>不足 9,547,200次操作&#x2F;小时</strong>.</li><li>最坏情况下的延迟变成了 <strong>1,610 ms</strong>, 而不再是 <strong>560ms</strong>。</li></ul><p>通过对这三个维度的介绍, 你应该了解, 不是简单的进行“性能(performance)”优化, 而是需要从三种不同的维度来进行考虑, 测量, 并调优延迟和吞吐量, 此外还需要考虑系统容量的约束。</p></article><div class="post-copyright"><div class="copyright-cc-box"><i class="anzhiyufont anzhiyu-icon-copyright"></i></div><div class="post-copyright__author_box"><a class="post-copyright__author_img" href="/" title="头像"><img class="post-copyright__author_img_back" src= "" onerror="this.onerror=null,this.src=&quot;/img/404.jpg&quot;" data-lazy-src="https://q1.qlogo.cn/g?b=qq&amp;nk=2071916845&amp;s=640" title="头像" alt="头像"><img class="post-copyright__author_img_front" src= "" onerror="this.onerror=null,this.src=&quot;/img/404.jpg&quot;" data-lazy-src="https://q1.qlogo.cn/g?b=qq&amp;nk=2071916845&amp;s=640" title="头像" alt="头像"></a><div class="post-copyright__author_name">云少</div><div 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你无需删除空行，直接评论以获取最佳展示效果</span></div></div><div class="comment-wrap"><div><div id="twikoo-wrap"></div></div><div><div id="artalk-wrap"></div></div></div></div><div class="comment-barrage"></div></div><div class="aside-content" id="aside-content"><div class="sticky_layout"><div class="card-widget" id="card-toc"><div class="item-headline"><i class="anzhiyufont anzhiyu-icon-bars"></i><span>文章目录</span><span class="toc-percentage"></span></div><div class="toc-content is-expand"><ol class="toc"><li class="toc-item toc-level-1"><a class="toc-link" href="#%E5%85%AD%E3%80%81GC-%E6%80%A7%E8%83%BD%E4%BC%98%E5%8C%96-%E2%80%93-GC-%E8%B0%83%E4%BC%98-%E5%9F%BA%E7%A1%80%E7%AF%87"><span class="toc-number">1.</span> <span class="toc-text">六、GC 性能优化 – GC 调优(基础篇)</span></a><ol class="toc-child"><li class="toc-item toc-level-2"><a class="toc-link" href="#%E6%A0%B8%E5%BF%83%E6%A6%82%E5%BF%B5-Core-Concepts"><span class="toc-number">1.1.</span> <span class="toc-text">核心概念(Core Concepts)</span></a><ol class="toc-child"><li class="toc-item toc-level-3"><a class="toc-link" href="#Latency-%E5%BB%B6%E8%BF%9F"><span class="toc-number">1.1.1.</span> <span class="toc-text">Latency(延迟)</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#Throughput-%E5%90%9E%E5%90%90%E9%87%8F"><span class="toc-number">1.1.2.</span> <span class="toc-text">Throughput(吞吐量)</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#Capacity-%E7%B3%BB%E7%BB%9F%E5%AE%B9%E9%87%8F"><span class="toc-number">1.1.3.</span> <span class="toc-text">Capacity(系统容量)</span></a></li></ol></li><li class="toc-item toc-level-2"><a class="toc-link" href="#%E7%9B%B8%E5%85%B3%E7%A4%BA%E4%BE%8B"><span class="toc-number">1.2.</span> <span class="toc-text">相关示例</span></a><ol class="toc-child"><li class="toc-item toc-level-3"><a class="toc-link" href="#Tuning-for-Latency-%E8%B0%83%E4%BC%98%E5%BB%B6%E8%BF%9F%E6%8C%87%E6%A0%87"><span class="toc-number">1.2.1.</span> <span class="toc-text">Tuning for Latency(调优延迟指标)</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#Tuning-for-Throughput-%E5%90%9E%E5%90%90%E9%87%8F%E8%B0%83%E4%BC%98"><span class="toc-number">1.2.2.</span> <span class="toc-text">Tuning for Throughput(吞吐量调优)</span></a></li><li class="toc-item toc-level-3"><a class="toc-link" href="#Tuning-for-Capacity-%E8%B0%83%E4%BC%98%E7%B3%BB%E7%BB%9F%E5%AE%B9%E9%87%8F"><span class="toc-number">1.2.3.</span> <span class="toc-text">Tuning for Capacity(调优系统容量)</span></a></li></ol></li></ol></li></ol></div></div></div></div></div></main><footer id="footer"><div id="footer-wrap"><div id="footer_deal"><a class="deal_link" href="mailto:2071916845@qq.com" rel="external nofollow noreferrer" title="email"><i class="anzhiyufont anzhiyu-icon-envelope"></i></a><a class="deal_link" target="_blank" rel="noopener external nofollow noreferrer" href="https://weibo.com/" title="微博"><i class="anzhiyufont anzhiyu-icon-weibo"></i></a><a class="deal_link" target="_blank" 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